Infinite randomness critical behavior of the contact process on networks with long-range connections

TL;DR

This study investigates the critical behavior of the contact process on long-range networks with random transition rates, revealing an infinite randomness fixed point characterized by logarithmic scaling, with critical exponents depending on network topology.

Contribution

It demonstrates that the infinite randomness critical behavior persists despite additional disorder, extending the applicability of the strong disorder renormalization group method to disordered networks.

Findings

Critical behavior described by an infinite randomness fixed point.

Critical exponents vary with the network's graph dimension.

Disorder in transition rates does not alter the infinite randomness critical behavior.

Abstract

The contact process and the slightly different susceptible-infected-susceptible model are studied on long-range connected networks in the presence of random transition rates by means of a strong disorder renormalization group method and Monte Carlo simulations. We focus on the case where the connection probability decays with the distance $l$ as $p(l)\simeq\beta l^{-2}$ in one dimension. Here, the graph dimension of the network can be continuously tuned with $\beta$. The critical behavior of the models is found to be described by an infinite randomness fixed point which manifests itself in logarithmic dynamical scaling. Estimates of the complete set of the critical exponents, which are found to vary with the graph dimension, are provided by different methods. According to the results, the additional disorder of transition rates does not alter the infinite randomness critical behavior induced by the disordered topology of the underlying random network. This finding opens up the possibility of the application of an alternative tool, the strong disorder renormalization group method to dynamical processes on topologically disordered structures. As the random transverse-field Ising model falls into the same universality class as the random contact process, the results can be directly transferred to that model defined on the same networks.